Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2409.14572.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:31.082625Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 88e1ddc9-39df-4998-85a0-00aced3a962a · inbound
Seeing Beyond Words: MatVQA for Challenging Visual-Scientific Reasoning in Materials Science cites this paper.
Seeing Beyond Words: MatVQA for Challenging Visual-Scientific Reasoning in Materials Science Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:37:31.082625Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e05d962-abcf-4f19-8425-f42ec50d2212 · inbound
URSA: The Universal Research and Scientific Agent cites this paper.
URSA: The Universal Research and Scientific Agent Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T07:22:09.404368Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b97c8d68-0e86-40d3-ae96-8d729f748927 · inbound
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-06-27T17:31:07.418673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fef9373f-781e-4f11-990b-e031991706e1 · inbound
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:03:26.840730Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.